• DocumentCode
    2872213
  • Title

    Combining Salient Points and Visual Perception Texture Features for Image Retrieval

  • Author

    Jian, Muwei ; Sun, Jiquan ; Liu, Jing ; Yin, Cheng ; Qin, Yan

  • Author_Institution
    Sch. of Space Sci. & Phys., Shandong Univ. at Weihai, Weihai, China
  • Volume
    2
  • fYear
    2009
  • fDate
    18-19 July 2009
  • Firstpage
    51
  • Lastpage
    54
  • Abstract
    This paper researches on content-based image retrieval based on salient points and visual perception texture features. First, we analysis local features for image indexing. In content-based image retrieval, the method based on salient points detection is one of the most active research areas for it can represent the local properties of the image. This paper introduces an improved salient points detector based on wavelet transform. Second, we introduce some new texture features based on wavelet transformdasias subbands which coincide with human eyes perception. Then, color moments and the introduced visual perception texture features of the regions around the salient points were computed as a features vector used for indexing the image. We tested the proposed scheme using a wide range image samples from the Corel image library, the experimental results indicating that the method has produced a promising result.
  • Keywords
    content-based retrieval; image colour analysis; image retrieval; image texture; object detection; wavelet transforms; color moment; content-based image retrieval; image indexing; salient point detection; visual perception texture feature; wavelet transform; Content based retrieval; Detectors; Eyes; Humans; Image analysis; Image retrieval; Indexing; Testing; Visual perception; Wavelet transforms; content-based; image retrieval; salient points; visual perception texture features; wavelet transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-0-7695-3699-6
  • Type

    conf

  • DOI
    10.1109/APCIP.2009.149
  • Filename
    5197134